Analisis Sentimen Kenaikan Harga Beras di Facebook Menggunakan Algoritma Naïve Bayes - Rijal; Eka Prasetyaningrum; Agung Purwanto; Abdul Aziz (Computer Science and Information Technology, 2024)
This study explores public sentiment towards the rice price increase in Indonesia using data from social media posts on Facebook. As a crucial staple commodity, rice prices significantly impact the economy and social life of the community. In this study, data was collected from Facebook using Instant Data Scraper during the period from January to May. The collected data underwent a cleaning process, and 200 data points were manually labeled as training data. The text preprocessing steps included tokenization, case folding, and stopword removal. Subsequently, TF-IDF weighting was applied to determine the importance of each word in the documents. The processed data was then analyzed using the Naive Bayes algorithm to classify positive and negative sentiments. The analysis results showed that out of 428 test data points, the Naive Bayes algorithm successfully identified 237 reviews as positive sentiment and 191 reviews as negative sentiment. Based on the obtained data, this study is expected to provide insights for the government and policymakers in managing rice price policies and improving public communication strategies, as well as anticipating the social impact of rice price increases.